Evaluating 40 Years of Wetland Conservation Policy (1981–2025)
A Spatio-Temporal EDA of India’s RAMSAR Sites
Author
Shashank Venkatesh
Published
December 19, 2025
Data Source
The foundational data for this analysis is sourced from the Open Government Data (OGD) Platform India catalog on Ramsar Sites Details. This dataset provides the geometric coordinates, area in hectares, and designation dates for the wetlands, allowing for a precise spatial and temporal analysis.
to ensure all “geometric coordinates” and sizes are correctly typed.
Geographical Distribution of Wetlands in India
The above map illustrates that India’s RAMSAR sites are not uniformly distributed but are clustered. We can see a concentration of sites in the Indo-Gangetic Plain and a significant concentration along the Southern Coast (Tamil Nadu). The nodal points also indicate that the largest wetlands (by area) are mostly located in the North and East, while the South is characterized by a cluster of smaller sites.
The Acceleration of Conservation: A Temporal Growth Analysis
How has the rate of RAMSAR designations in India changed over the last three decades, particularly since the 2020s?
to pinpoint the “inflection point” where India’s wetland conservation policy shifted into high gear.
It is apparent from the above plot that a Policy Inflection Point may have occurred around 2020. While designations were sporadic from 1981 to 2010, the curve steepens sharply in the last five years with more than half (59) of India’s current Ramsar sites designated since 2020, signaling an aggressive shift in national environmental policy and a heightened commitment to international conservation standards.
As the plot confirms, before 2020 (The Legacy Era), we only had 37 RAMSAR sites. This represents nearly 40 years of gradual identification and designation since India joined the convention in 1982. However, post-2020 (The Modern Era), we can see that 59 additional sites were designated as RAMSAR sites, which forms 61% of India’s total RAMSAR sites were declared in just the last 5 years. This sudden surge indicates a shift in environmental priority, where the administrative speed of protecting wetlands has tripled compared to the previous four decades.
State-wise Distribution: The Pareto of Wetlands
Does a small number of states hold the majority of India’s RAMSAR designations, or is it evenly distributed?
Count sites by StateName and sort them in descending order.
Highlights regional leaders in ecological preservation.
The bar plot confirms a disparity in wetland distribution. Tamil Nadu leads the country with 20 sites, followed by Uttar Pradesh (10). These top two states have 31.2% of the total number of RAMSAR sites in India. Further, the share of the top 5 states in this metric is an overwhelming —50.0%. Conversely, 50.0% of states (13) have only 1or 2 sites, suggesting that conservation success is currently driven by specific state-level initiatives rather than a perfectly uniform national spread.
The Size Spectrum: Are we protecting large basins or small habitats?
What is the distribution of wetland sizes (Area), and are there “mega-wetlands” that skew the national average?
Transform the Area column to numeric and create a histogram or density plot.
Shows if India focuses on massive ecological systems or smaller, niche biodiversity hotspots.
Latitude & Area: The North-South Conservation Divide
Is there a relationship between geographical latitude and the size of designated wetlands?
Helps visualize if northern (Himalayan) wetlands tend to be larger or smaller than southern/peninsular ones.
The Log-scaled histogram reveals that India protects a wide “Size Spectrum.” Most sites fall within the 1,000 to 10,000-hectare range. However, the presence of sites on the extreme right of the log scale indicates “Mega-Wetlands” (massive river basins or lakes) that serve as critical national ecological anchors, while the smaller peaks represent specialized bird sanctuaries or niche habitats.
District-Level Granularity within Leading States
Within the states with the most RAMSAR sites, are the wetlands clustered in specific districts or spread across the state?
Identifies “ecological hubs” at a local government level, which is useful for management authority analysis.
The scatter plot with the regression line shows a fascinating geospatial trend: Northern wetlands tend to be larger in area. As latitude increases, we see more “Mega-Wetlands.” This is likely due to the vast floodplains of the Himalayan river systems. In contrast, southern latitudes (closer to the equator) feature a higher number of smaller, fragmented coastal and tank ecosystems.
The “Anthropogenic Shift”: Natural vs. Human-Made
This analysis reveals if India is increasingly relying on man-made reservoirs for international conservation status and determines if India’s conservation strategy is shifting toward protecting infrastructure-based biodiversity.
Recent additions like Siliserh Lake (Rajasthan) and Kopra Reservoir (Chhattisgarh) are artificial ecosystems.
What percentage of India’s RAMSAR network is comprised of human-made “Artificial” wetlands, and how does their average size compare to natural basins?
The violin-and-box plot reveals that human-made reservoirs and barrages are now statistically comparable in importance to natural lakes. The Human-Made / Managed category shows a dense cluster of designations, proving that India is successfully leveraging its irrigation infrastructure (tanks and reservoirs) to provide international-standard habitats for migratory species.
Semantic Analysis of Uniqueness: The Biodiversity Fingerprint
A good approach to understand the uniqueness of India’s RAMSAR sites is by performing Natural Language Processing (NLP) on the qualitative data (categorical data) contained in our dataset. This can be done by extracting and identifying specific keywords from the observation “Uniqueness” of the dataset that justify why these 96 sites were recognized by awarding them with international status.
What are the top three Ecological Drivers (e.g., Birds, Flood Control) that qualify an Indian site for RAMSAR status?
We extract the keywords of Uniqueness Observation to see what makes a wetland internationally significant in the Indian context.
The high frequency of terms like “bird/birds/waterbirds” (appears 14 times),“sanctuary/reservoir” (appears 9 times), and “migratory” (appears 4 times), confirms that India’s RAMSAR sites are primarily designated because of their role as bird sanctuaries in the Central Asian Flyway. This also signals that a large portion of India’s protected wetlands are irrigation reservoirs and designated sanctuaries, integrated into international conservation frameworks. Further, the appearance of words like “freshwater” (5 times) and “endangered” (3 times) indicates that the often used logic behind India’s conservation framework of wetlands is threat-response, where the attention is focused on inland freshwater ecosystems (not just coastal) and their role in preventing the extinction of specific species.
The “Seasonality” of Global Recognition
Is there a “Policy Momentum” effect where sites are more likely to be designated around specific international events (e.g., World Wetlands Day in February or Independence Day)?
Reveals if political timelines influence ecological milestones. A radial plot to see if designations cluster around specific months (Policy Momentum).
The radial chart shows that Ramsar designations are not distributed evenly throughout the year. There is a visible clustering in specific months (often aligning with World Wetlands Day in February or year-end policy cycles). This suggests that the “timing” of conservation is often tied to global environmental milestones and diplomatic windows.
Fragmented vs. Clustered Landscapes (Spatial Join)
Are Indian RAMSAR sites becoming “Ecological Corridors” (closely clustered) or “Isolated Oases”?
Identify Hotspots (like the Tamil Nadu belt) versus Isolated sites (like Pala Wetland in Mizoram). This calculates how far each site is from its closest “Conservation Neighbor” to identify hubs.
The “Isolation Index” identifies which sites are “Oases” and which are “Corridors.” Sites in Tamil Nadu and Punjab show very low “distance to nearest neighbor,” indicating they form a functional ecological network. In contrast, the high isolation of sites like Pala Wetland (Mizoram) highlights their role as unique, standalone biodiversity refuges that require specialized, independent management strategies.
Management Diversity & Scale
The dataset names various authorities, from “Retired Academics” to “Forest Departments.”
Does the type of ManagementAuthority correlate with the Total Area protected? Do institutional authorities manage larger “Mega-Wetlands” while local/academic bodies manage smaller “Niche Sanctuaries”?
The jittered scatter plot shows a clear institutional division: State Forest Departments manage the widest range of sites, including the “Mega-Wetlands.” However, “Other/Local Authorities” and “Academic/Expert” bodies are increasingly involved in managing smaller, niche sanctuaries. This indicates a decentralization of conservation, where specialized knowledge is being used to protect smaller but ecologically “loud” sites.
Source Code
---title: "Evaluating 40 Years of Wetland Conservation Policy (1981–2025)"subtitle: "A Spatio-Temporal EDA of India’s RAMSAR Sites"author: "Shashank Venkatesh"date: last-modifiedformat: html: theme: cosmo toc: true toc-depth: 2 toc-location: left code-fold: false code-tools: true embed-resources: trueexecute: echo: false warning: false message: falsebibliography: references.bibeditor_options: chunk_output_type: console---## Data SourceThe foundational data for this analysis is sourced from the **Open Government Data (OGD) Platform India** catalog on **Ramsar Sites Details**.This dataset provides the geometric coordinates, area in hectares, and designation dates for the wetlands, allowing for a precise spatial and temporal analysis.```{r}#| label: load the datasetlibrary(tidyverse)library(tidymodels)library(scales) # For formatting legend numberslibrary(jsonlite)library(sf)#library(rnaturalearth) # Error in Indian map with wrong administrative boundaries#library(rnaturalearthdata) #library(mapindia)library(ggrepel) # For non-overlapping labelslibrary(plotly)library(tidytext)library(patchwork) # To combine plots if neededlibrary(ggwordcloud)# URL of the RAMSAR APIapi_url <-"https://indianwetlands.in/wp-content/themes/wetlands/restapi-ramsar.php"# Fetch and parse the data# Note: Sometimes government APIs require a 'User-Agent' header to allow accessramsar_json <-fromJSON(api_url)# Convert to a data frame# The API usually returns a nested list; we simplify it hereramsar_data <-as.data.frame(ramsar_json)```## Glimpse of the dataframe```{r}#| label: glimpse into the dataframe#| message: false#| warning: falseglimpse(ramsar_data)```The dataset has **`r nrow(ramsar_data)`** rows and **`r ncol(ramsar_data)`** columns.## Data Cleaning and Preparationto ensure all "geometric coordinates" and sizes are correctly typed.```{r}#| label: Converting chr strings to proper data types#| message: false#| warning: false# Convert character strings to numeric and date typesramsar_clean <- ramsar_data |>mutate(Area =as.numeric(Area),Latitude =as.numeric(Latitude),Longitude =as.numeric(Longitude),Date =as.Date(DesignationDate),Year =as.numeric(format(Date, "%Y")) ) |>arrange(Date) |>mutate(cumulative_sites =row_number())```## Geographical Distribution of Wetlands in India```{r}#| label: Geographical Distribution of wetlands in India#| eval: false#| include: false# 2. Get India's high-resolution boundary (sf format)india_map <-ne_countries(scale ="large", country ="India", returnclass ="sf")# 3. Convert our RAMSAR dataframe into a spatial object (sf)# We ensure Latitude/Longitude are numeric firstramsar_sf <- ramsar_clean |>filter(!is.na(Latitude) &!is.na(Longitude)) |>st_as_sf(coords =c("Longitude", "Latitude"), crs =4326)# 4. Plotting the Geometric Mapggplot() +# Draw the base map of India with a minimalist geometric stylegeom_sf(data = india_map, fill ="#F8F9FA", color ="#ADB5BD", size =0.3) +# Plot wetlands as geometric bubbles# Size is proportional to the Area; color is our signature Tealgeom_sf(data = ramsar_sf, aes(size = Area), color ="#2A9D8F", alpha =0.6) +# Add labels only for the top 5 largest sites to keep it "geometric and clean"geom_text_repel(data = ramsar_sf |>slice_max(Area, n =5),aes(label = WetlandName, geometry = geometry),stat ="sf_coordinates",size =3, fontface ="bold",family ="sans",color ="#264653",box.padding =0.5) +# Formatting the scales and legendscale_size_continuous(range =c(1, 12), labels =label_comma(),name ="Area (Hectares)") +# Apply a minimalist void themetheme_void() +labs(title ="GEOSPATIAL ARCHITECTURE OF INDIA'S RAMSAR SITES",subtitle ="Analysis of 96 internationally significant wetlands as of Dec 2025",caption ="Data Source: Open Government Data (OGD) Platform India | Projection: WGS84" ) +# Styling the typographytheme(plot.title =element_text(family ="sans", face ="bold", size =18, color ="#264653", hjust =0.5),plot.subtitle =element_text(family ="sans", size =11, color ="#666666", hjust =0.5, margin =margin(b =20)),legend.position =c(0.85, 0.2),plot.background =element_rect(fill ="white", color =NA) )``````{r}# 1. Retrieve the official state boundaries as an sf object# This function is specifically designed to provide the official Indian mapindia_official <-map_india(regions ="states")#Convert our RAMSAR dataframe into a spatial object (sf)# We ensure Latitude/Longitude are numeric firstramsar_sf <- ramsar_clean |>filter(!is.na(Latitude) &!is.na(Longitude)) |>st_as_sf(coords =c("Longitude", "Latitude"), crs =4326)# 2. Plotting the correct geometryggplot() +# Draw the base map with the official boundariesgeom_sf(data = india_official, fill ="#F8F9FA", color ="#ADB5BD", size =0.3) +# Layer the RAMSAR sites as geometric nodesgeom_sf(data = ramsar_sf, aes(size = Area), color ="#2A9D8F", alpha =0.6) +theme_void() +labs(title ="OFFICIAL SPATIAL ARCHITECTURE: RAMSAR SITES",subtitle ="Analysis using official Survey of India administrative boundaries",caption ="Data Source: mapindia package & OGD India" ) +theme(plot.title =element_text(face ="bold", size =16, color ="#264653", hjust =0.5),legend.position ="bottom" )```The above map illustrates that India's RAMSAR sites are not uniformly distributed but are clustered. We can see a concentration of sites in the **Indo-Gangetic Plain** and a significant concentration along the **Southern Coast (Tamil Nadu)**. The nodal points also indicate that the largest wetlands (by area) are mostly located in the North and East, while the South is characterized by a cluster of smaller sites.## The Acceleration of Conservation: A Temporal Growth Analysis### How has the rate of RAMSAR designations in India changed over the last three decades, particularly since the 2020s?to pinpoint the "inflection point" where India’s wetland conservation policy shifted into high gear.```{r}#| label: temporal growth analysis#| message: false#| warning: falseggplot(ramsar_clean, aes(x = Date, y = cumulative_sites)) +#geom_area(fill = "#2A9D8F", alpha = 0.2) +geom_line(color ="#2A9D8F", size =0.5) +geom_point(data =filter(ramsar_clean, Year >=2020), color ="#E63946", size =1) +labs(title ="The Exponential Rise of Indian RAMSAR Sites",subtitle ="Cumulative designations from 1981 to December 2025",x ="Year of Designation", y ="Total Number of Sites" ) +theme_minimal() +theme(panel.grid.minor =element_blank(),plot.title =element_text(face ="bold", size =16, color ="#264653"))```It is apparent from the above plot that a **Policy Inflection Point may have occurred around 2020**. While designations were sporadic from 1981 to 2010, the curve steepens sharply in the last five years with more than half (59) of India's current Ramsar sites designated since 2020, signaling an aggressive shift in national environmental policy and a heightened commitment to international conservation standards.```{r}#| label: temporal analysis of designation # 1. Categorize the sites based on the 2020 inflection pointramsar_phases <- ramsar_clean |>mutate(Phase =ifelse(Year <2020, "Before 2020", "Since 2020")) |>count(Phase) |>mutate(Percentage = n /sum(n) *100)# 2. Print the summary table to console#print(ramsar_phases)# 3. Geometric Vector Visualizationggplot(ramsar_phases, aes(x = Phase, y = n, fill = Phase)) +# Using a fixed width for a clean geometric lookgeom_col(width =0.6, color ="#264653", size =1) +# Adding count labels directly on top of the barsgeom_text(aes(label = n), vjust =-0.5, size =6, fontface ="bold", color ="#264653") +ylim(0, 70) +# Applying the Teal and Coral palettescale_fill_manual(values =c("Before 2020"="#2A9D8F", "Since 2020"="#E63946")) +labs(title ="PHASE ANALYSIS: THE 2020 CONSERVATION PIVOT",subtitle ="Comparing decades of legacy designations vs. recent geometric growth",x =NULL, y ="Number of Designated Sites" ) +theme_minimal() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =16, color ="#264653"),panel.grid.major.x =element_blank(),axis.text.x =element_text(size =12, face ="bold", color ="#264653") )```As the plot confirms, before 2020 (The Legacy Era), we only had 37 RAMSAR sites. This represents nearly 40 years of gradual identification and designation since India joined the convention in 1982. However, post-2020 (The Modern Era), we can see that 59 additional sites were designated as RAMSAR sites, which forms **61% of India’s total RAMSAR sites were declared in just the last 5 years.** This sudden surge indicates a shift in environmental priority, where the administrative speed of protecting wetlands has tripled compared to the previous four decades.## State-wise Distribution: The Pareto of Wetlands### Does a small number of states hold the majority of India's RAMSAR designations, or is it evenly distributed?Count sites by `StateName` and sort them in descending order.Highlights regional leaders in ecological preservation.```{r}#| label: State-wise Distributionramsar_clean |>count(StateName) |>mutate(Highlight =ifelse(n >=10, "Top Tier", "Other")) |>ggplot(aes(x = n, y =reorder(StateName, n), fill = Highlight)) +geom_col(width =0.8) +scale_fill_manual(values =c("Top Tier"="#E63946", "Other"="#2A9D8F")) +labs(title ="Regional Distribution of Wetlands",subtitle ="Tamil Nadu leads the geometric distribution with 20 sites",x ="Number of Designated Sites", y =NULL, fill ="Status" ) +theme_minimal() +theme(legend.position ="none",axis.text.y =element_text(size =9, face ="bold"))``````{r}#| label: states with highest wetland sites#| echo: falsetop_states <- ramsar_clean |>group_by(StateName) |>summarise(TotalSites =n()) |>arrange(desc(TotalSites))top_two <-percent(sum(top_states$TotalSites[1:2])/sum(top_states$TotalSites[1:nrow(top_states)]), accuracy =0.1)top_five <-percent(sum(top_states$TotalSites[1:5])/sum(top_states$TotalSites[1:nrow(top_states)]), accuracy =0.1)niche_states_data <- ramsar_clean |>group_by(StateName) |>summarise(SiteCount =n()) |>filter(SiteCount %in%c(1, 2)) summary_counts <- niche_states_data |>group_by(SiteCount) |>summarise(NumberOfStates =n())total_states =nrow(top_states)one_or_two_states =sum(summary_counts$NumberOfStates[1:2])one_or_two_states_p =percent(one_or_two_states/total_states, accuracy =0.1)```The bar plot confirms a disparity in wetland distribution. **Tamil Nadu** leads the country with **`r top_states$TotalSites[1]` sites**, followed by Uttar Pradesh (**`r top_states$TotalSites[2]`**). These top two states have **`r top_two`** of the total number of RAMSAR sites in India. Further, the share of the top 5 states in this metric is an overwhelming —**`r top_five`**. Conversely, **`r one_or_two_states_p` of states (`r one_or_two_states`)** have only 1or 2 sites, suggesting that conservation success is currently driven by specific state-level initiatives rather than a perfectly uniform national spread.## The Size Spectrum: Are we protecting large basins or small habitats?### What is the distribution of wetland sizes (Area), and are there "mega-wetlands" that skew the national average?Transform the `Area` column to numeric and create a histogram or density plot.Shows if India focuses on massive ecological systems or smaller, niche biodiversity hotspots.```{r}#| label: Large basins v/s small habitatsggplot(ramsar_clean, aes(x = Area)) +geom_histogram(fill ="#264653", color ="white", bins =20) +scale_x_log10(labels = comma) +labs(title ="Distribution of Wetland Area (Log Scale)",subtitle ="Analysis of size variance from small reserves to massive basins",x ="Area in Hectares (Log10)", y ="Frequency of Sites" ) +theme_minimal() +theme(panel.grid.minor =element_blank())```## Latitude & Area: The North-South Conservation Divide### Is there a relationship between geographical latitude and the size of designated wetlands?Helps visualize if northern (Himalayan) wetlands tend to be larger or smaller than southern/peninsular ones.```{r}#| label: geographical distributionggplot(ramsar_clean, aes(x = Latitude, y = Area)) +geom_point(aes(size = Area), color ="#2A9D8F", alpha =0.5) +geom_smooth(method ="lm", color ="#E63946", se =FALSE, linetype ="dashed") +scale_y_log10(labels = comma) +labs(title ="Geospatial Area Trends",subtitle ="Correlation between Latitude and Wetland Scale",x ="Latitude (Degrees North)", y ="Area (Hectares)" ) +theme_minimal() +guides(size ="none")```The Log-scaled histogram reveals that India protects a wide "Size Spectrum." Most sites fall within the **1,000 to 10,000-hectare range**. However, the presence of sites on the extreme right of the log scale indicates "Mega-Wetlands" (massive river basins or lakes) that serve as critical national ecological anchors, while the smaller peaks represent specialized bird sanctuaries or niche habitats.## District-Level Granularity within Leading States### Within the states with the most RAMSAR sites, are the wetlands clustered in specific districts or spread across the state?Identifies "ecological hubs" at a local government level, which is useful for management authority analysis.```{r}#| label: Identifying ecological hubsramsar_clean |>filter(StateName =="Tamil Nadu") |>count(districtname) |>ggplot(aes(x = n, y =reorder(districtname, n))) +geom_segment(aes(x =0, xend = n, yend = districtname), color ="#2A9D8F") +geom_point(size =4, color ="#E63946") +labs(title ="Local Concentration: Tamil Nadu",subtitle ="District-wise breakdown of sites in India's leading RAMSAR state",x ="Number of Sites", y =NULL ) +theme_minimal() +theme(panel.grid.major.y =element_blank())```The scatter plot with the regression line shows a fascinating geospatial trend: **Northern wetlands tend to be larger in area.** As latitude increases, we see more "Mega-Wetlands." This is likely due to the vast floodplains of the Himalayan river systems. In contrast, southern latitudes (closer to the equator) feature a higher number of smaller, fragmented coastal and tank ecosystems.## The "Anthropogenic Shift": Natural vs. Human-MadeThis analysis reveals if India is increasingly relying on man-made reservoirs for international conservation status and determines if India’s conservation strategy is shifting toward protecting infrastructure-based biodiversity.Recent additions like **Siliserh Lake** (Rajasthan) and **Kopra Reservoir** (Chhattisgarh) are artificial ecosystems.### What percentage of India's RAMSAR network is comprised of human-made "Artificial" wetlands, and how does their average size compare to natural basins?```{r}#| label: Artificial wetlands# 1. Classification Logicramsar_cat <- ramsar_clean |>mutate(Type =case_when(str_detect(WetlandName, "Reservoir|Dam|Barrage|Tank|Canal|Reserve") ~"Human-Made / Managed",TRUE~"Natural / Semi-Natural" ))# 2. Visualizationggplot(ramsar_cat, aes(x = Type, y = Area, fill = Type)) +geom_violin(alpha =0.3, color =NA) +geom_boxplot(width =0.1, color ="#264653", outlier.shape =NA) +geom_jitter(aes(color = Type), alpha =0.5, width =0.15) +scale_y_log10(labels =label_comma()) +scale_fill_manual(values =c("Human-Made / Managed"="#E63946", "Natural / Semi-Natural"="#2A9D8F")) +scale_color_manual(values =c("Human-Made / Managed"="#E63946", "Natural / Semi-Natural"="#2A9D8F")) +labs(title ="Scale Architecture: Natural vs. Managed Ecosystems",subtitle ="Comparison of total area distribution (Hectares) across 96 sites",x =NULL, y ="Area (Hectares, Log Scale)") +theme_minimal() +theme(legend.position ="none", plot.title =element_text(face ="bold"))```The violin-and-box plot reveals that human-made reservoirs and barrages are now statistically comparable in importance to natural lakes. The **Human-Made / Managed** category shows a dense cluster of designations, proving that India is successfully leveraging its irrigation infrastructure (tanks and reservoirs) to provide international-standard habitats for migratory species.## Semantic Analysis of Uniqueness: The Biodiversity FingerprintA good approach to understand the uniqueness of India's RAMSAR sites is by performing Natural Language Processing (NLP) on the qualitative data (categorical data) contained in our dataset. This can be done by extracting and identifying specific keywords from the observation "Uniqueness" of the dataset that justify why these 96 sites were recognized by awarding them with international status.```{r}#| label: Geometric Word Cloud#| echo: false# 1. Update the list with strictly lowercase termsexclude_words <-c("wetland", "ramsar", "india", "sites", "lake", "water", "ground")# 2. Text Mininguniqueness_words <- ramsar_clean |>unnest_tokens(word, Uniqueness) |>anti_join(stop_words) |>filter(!word %in% exclude_words) |>count(word, sort =TRUE) |>head(15)# 1. Generate the Geometric Word Cloudggplot(uniqueness_words, aes(label = word, size = n, color = n)) +# Using the 'area' shape for a more geometric, structured distributiongeom_text_wordcloud_area(area_corr =TRUE, shape ="square", rm_outside =TRUE) +# Scaling the text size geometricallyscale_size_area(max_size =50) +# Applying Teal and Coral color palettescale_color_gradient(low ="#2A9D8F", high ="#E63946") +# Minimalist formattingtheme_void() +theme(plot.background =element_rect(color =NA) )```### What are the top three Ecological Drivers (e.g., Birds, Flood Control) that qualify an Indian site for RAMSAR status?We extract the keywords of Uniqueness Observation to see what makes a wetland internationally significant in the Indian context.```{r}#| label: analyse the uniqueness# 2. Geometric Bar Plotggplot(uniqueness_words, aes(x = n, y =reorder(word, n))) +geom_col(fill ="#264653") +geom_text(aes(label = n), hjust =-0.2, size =3, color ="#264653") +labs(title ="The Vocabulary of Conservation",subtitle ="Top recurring terms in site uniqueness descriptions",x ="Frequency of Mention", y =NULL) +theme_minimal() +theme(panel.grid.major.y =element_blank())```The high frequency of terms like **"bird/birds/waterbirds" (appears 14 times),** **"sanctuary/reservoir" (appears 9 times)**, and **"migratory" (appears 4 times)**, confirms that India’s RAMSAR sites are primarily designated because of their role as bird sanctuaries in the Central Asian Flyway. This also signals that a large portion of India’s protected wetlands are irrigation reservoirs and designated sanctuaries, integrated into international conservation frameworks. Further, the appearance of words like **"freshwater" (5 times)** and **"endangered" (3 times)** indicates that the often used logic behind India's conservation framework of wetlands is threat-response, where the attention is focused on inland freshwater ecosystems (not just coastal) and their role in preventing the extinction of specific species.## The "Seasonality" of Global Recognition### Is there a "Policy Momentum" effect where sites are more likely to be designated around specific international events (e.g., World Wetlands Day in February or Independence Day)?Reveals if political timelines influence ecological milestones. A radial plot to see if designations cluster around specific months (Policy Momentum).```{r}#| label: seasonality-analysisramsar_months <- ramsar_clean |>mutate(Month =month(Date, label =TRUE)) |>count(Month)ggplot(ramsar_months, aes(x = Month, y = n, fill = n)) +geom_col(show.legend =FALSE) +coord_polar() +scale_fill_gradient(low ="#8ECAE6", high ="#219EBC") +labs(title ="The Annual Cycle of Designations",subtitle ="Frequency of RAMSAR announcements by month",x =NULL, y =NULL) +theme_minimal() +theme(axis.text.y =element_blank())```The radial chart shows that Ramsar designations are not distributed evenly throughout the year. There is a visible clustering in specific months (often aligning with **World Wetlands Day in February** or year-end policy cycles). This suggests that the "timing" of conservation is often tied to global environmental milestones and diplomatic windows.## Fragmented vs. Clustered Landscapes (Spatial Join)### Are Indian RAMSAR sites becoming "Ecological Corridors" (closely clustered) or "Isolated Oases"?Identify Hotspots (like the Tamil Nadu belt) versus Isolated sites (like Pala Wetland in Mizoram). This calculates how far each site is from its closest "Conservation Neighbor" to identify hubs.```{r}#| label: Ecological Corridors# 1. Calculate distancescoords <-st_coordinates(ramsar_sf)dist_matrix <-st_distance(ramsar_sf)diag(dist_matrix) <-Inf# Ignore distance to selframsar_clean$min_dist_km <-apply(dist_matrix, 1, min) /1000# 2. Plotting the 'Isolation' indexggplot(ramsar_clean, aes(x =reorder(WetlandName, min_dist_km), y = min_dist_km)) +geom_point(color ="#E63946") +geom_segment(aes(xend = WetlandName, yend =0), color ="#CED4DA", alpha =0.5) +coord_flip() +labs(title ="Proximity and Ecological Corridors",subtitle ="Distance to nearest RAMSAR site (km) - Top 20 most isolated sites",x =NULL, y ="Distance to Nearest Neighbor (km)") +theme_minimal() +scale_x_discrete(breaks = ramsar_clean$WetlandName[seq(1, 96, by =5)]) # Show every 5th for clarity```The "Isolation Index" identifies which sites are "Oases" and which are "Corridors." Sites in **Tamil Nadu and Punjab** show very low "distance to nearest neighbor," indicating they form a functional ecological network. In contrast, the high isolation of sites like **Pala Wetland (Mizoram)** highlights their role as unique, standalone biodiversity refuges that require specialized, independent management strategies.## Management Diversity & ScaleThe dataset names various authorities, from "Retired Academics" to "Forest Departments."### Does the type of `ManagementAuthority` correlate with the **Total Area** protected? Do institutional authorities manage larger "Mega-Wetlands" while local/academic bodies manage smaller "Niche Sanctuaries"?```{r}#| label: Visualizing if large basins are managed differently than smaller bird sanctuaries.# 1. Simplify Authority Categoriesramsar_auth <- ramsar_clean |>mutate(AuthType =case_when(str_detect(ManagementAuthority, "Forest|Department|Wildlife") ~"State Forest Dept",str_detect(ManagementAuthority, "University|Academic|Retired") ~"Academic/Expert",TRUE~"Other/Local Authority" ))# 2. Visualizationggplot(ramsar_auth, aes(x = AuthType, y = Area, color = AuthType)) +geom_jitter(size =3, alpha =0.6, width =0.2) +scale_y_log10(labels =label_comma()) +scale_color_manual(values =c("#264653", "#E63946", "#2A9D8F")) +labs(title ="Institutional Scale and Oversight",subtitle ="Wetland area managed by different authority types",x ="Authority Category", y ="Area (Hectares)") +theme_minimal() +theme(legend.position ="none", axis.text.x =element_text(face ="bold"))```The jittered scatter plot shows a clear institutional division: **State Forest Departments** manage the widest range of sites, including the "Mega-Wetlands." However, **"Other/Local Authorities"** and **"Academic/Expert"** bodies are increasingly involved in managing smaller, niche sanctuaries. This indicates a decentralization of conservation, where specialized knowledge is being used to protect smaller but ecologically "loud" sites.###
